294 results about "Dynamic filtering" patented technology

The invention discloses an unmanned aerial vehicle image small target detection method based on dynamic filtering and an adaptive sparse Transform. According to the method, an end-to-end target detection framework is adopted, a dynamic filtering module is introduced into a backbone network, global feature interaction is achieved through data-dependent frequency domain operation, and linear calculation complexity is maintained. For feature interaction in a scale, an adaptive sparse Transform module is introduced to enhance the capability of focusing key information on high semantic hierarchy features of a model, and noise interference and feature redundancy are effectively suppressed at the same time. Through the combination of dynamic filtering and adaptive sparse Transform, the model can extract image foreground information more effectively on the premise of not significantly increasing the calculation burden, and the problem that a traditional target detection model is susceptible to complex background interference is significantly relieved.

The invention relates to the technical field of agricultural machinery automation control, and discloses an agricultural machinery operation path intelligent planning method based on Beidou positioning. The method comprises the following steps: firstly, acquiring Beidou positioning data and a farmland geographic data set, and eliminating positioning noise by using a dynamic filtering algorithm to obtain a calibrated agricultural machine track sequence; performing rasterization processing on the farmland geographic data to generate a digital elevation model, and dividing a job priority region; generating an initial operation path by adopting a hybrid optimization algorithm, and dynamically adjusting the path based on real-time operation state information; and finally, a path conflict detection module is combined with obstacle coordinates to calculate a collision probability, and a final agricultural machine operation path is output. According to the method, various factors are comprehensively considered, real-time dynamic adjustment of the path can be realized, path conflicts are effectively avoided, the operation precision and efficiency of agricultural machinery are improved, energy consumption and equipment loss are reduced, and agricultural mechanization and intelligentization development is promoted.

The invention relates to the technical field of cable fault detection, in particular to a cable fault analysis system based on big data, which comprises a disturbance feature extraction module, a time sequence hierarchy judgment module, a reflection characteristic analysis module, a topological section identification module and a fault path mapping module. According to the method, gradient inflection points are extracted by calculating voltage and current increment included angles at adjacent moments, spatial-temporal characteristics of disturbance signals are correlated, interference of noise to a starting point mark is reduced, time difference is fitted in combination with cable length, deviation over-threshold abnormal points are dynamically filtered, influences of synchronization errors on sorting are eliminated, and fault path mapping stability is improved; the method comprises the following steps: generating characteristic parameters by fusing amplitude values and wave crest intervals of main waves and reflection waves, quantifying amplitude attenuation and phase offset coupling, enhancing the multi-path superposed signal separation capability, judging the reflection characteristic similarity of a physical section based on a Manhattan distance and a support vector machine, and dynamically dividing a fault section in combination with line topology. The problem of fuzzy positioning in a branch parallel scene in a traditional method is solved.

The invention relates to the technical field of automatic production control and industrial data processing, in particular to a PLC-based gypsum-based flame-retardant plate production line control system and method, and the system comprises a data collection module which collects sensor data in real time; the ideal state construction module is used for constructing a pure ideal state vector; the disturbance simulation module is used for generating a theoretical damaged state vector corresponding to a specific fault mode; the difference calculation module is used for generating real and theoretical deviation vectors; the coupling verification control module is used for calculating a similarity value between the actual deviation vector and the theoretical deviation vector; if the similarity value is larger than a preset judgment threshold value, it is judged that a real physical fault exists at present, and a PLC precise compensation instruction is generated and sent to a production line execution mechanism; otherwise, judging that non-physical noise exists at present, keeping the control parameters of the production line unchanged, and starting a dynamic filtering program; according to the method, the problem that physical faults and sensor clutters are difficult to distinguish in a high-noise environment is effectively solved, and missing report is avoided.

The invention relates to the technical field of power distribution networks, and discloses a power distribution network fault self-recovery method and system based on machine learning, and the method comprises the steps: collecting the multi-source fault feature data of a power distribution network in real time, including the voltage and current waveform distortion feature, the load sudden change feature and the equipment temperature anomaly feature; and inputting the multi-source fault feature data into a multi-stage classification model for processing, and by setting a pseudo fault filtering module, establishing a typical interference feature library and setting dynamic filtering thresholds for different working conditions during real-time fault detection of the power distribution network, so that the recognition accuracy of various interference signals is ensured, and the fault detection accuracy of the power distribution network is improved. Meanwhile, similarity comparison is carried out on real-time collected data and pseudo fault features, whether the problems of signal distortion and noise pollution occur at the fault positioning position of the power distribution network or not can be detected in real time, the accuracy of fault type recognition is ensured, the misjudgment risk is reduced, and when strategy adaptation is abnormal, the matching degree of the self-healing strategy and the actual state of the power grid is ensured.

The invention relates to the technical field of artificial intelligence and biomedical sensing, and discloses a cognitive disorder risk identification device and method integrating multi-modal physiological data, and the device comprises an EEG module, an eye movement module, an fNIRS module, an edge calculation module and an acceleration sensor; the EEG module collects EEG data, the eye movement module collects eye movement eye-tracing data, and the fNIRS module collects near infrared spectrum fNIRS data and inputs the data to the edge calculation module; the edge calculation module runs the multi-modal space-time attention fusion model to output a cognitive impairment risk assessment result by using the multi-modal space-time attention fusion model; the acceleration sensor operates based on an operation dynamic filtering algorithm of a motion accelerometer to inhibit signal drift caused by head motion. According to the invention, through the modularized head-mounted multi-mode edge device, the physiological data is collected and edge end processing and cognitive disorder recognition and screening are carried out in combination with the edge calculation module, so that early recognition and screening of neurodegenerative diseases can be conveniently and effectively carried out at low cost.

A data processing method, an electronic device, and a storage medium. The method comprises: receiving a query request, wherein the query request comprises a target data table participating in a join operation; on the basis of the query request, executing a logical plan, wherein the execution of the logical plan comprises: constructing a data filtering condition corresponding to column statistical information, the column statistical information being information obtained by means of compiling statistics in advance on a column value range of columns included in the target data table; and executing a physical plan corresponding to the logical plan, wherein the execution of the physical plan comprises: performing partition pruning and / or dynamic filtering on the target data table on the basis of the data filtering condition, so as to generate a processed target data table, and executing the join operation on the processed target data table, so as to obtain an execution result.

The invention discloses a turbofan engine compressor blade crack multi-modal monitoring system, and belongs to the field of multi-modal monitoring, and the system comprises a multi-modal acquisition module which obtains the multi-modal data of each compressor blade, carries out the dynamic filtering denoising and temperature compensation preprocessing, completes the time axis alignment, and carries out the data reconstruction and feature extraction; and constructing a multi-modal feature vector. And the triggering module carries out preliminary abnormality diagnosis according to the judgment result, judges whether the blade is abnormal or not through a key feature threshold, determines whether to trigger depth evaluation or not by combining a periodic threshold, identifies a potential crack blade and generates an excitation decision. And when an excitation decision is executed, the intelligent diagnosis module monitors the strain gradient change to perform excitation monitoring, compares the vibration characteristics of a potential crack blade with the vibration characteristics of a contrast blade, judges whether a crack exists or not, and realizes multi-stage early warning, so as to solve the problem that the crack of the turbofan engine compressor blade is difficult to quickly detect in real time in the prior art. And safe and stable operation of the engine is guaranteed.

The invention relates to the field of ocean observation data processing, and discloses an ocean salinity data correction method based on space-time gradient adaptive filtering, which comprises the following steps of: calculating a motion compensation salinity gradient and a motion compensation temperature gradient; constructing a double-gradient driving filtering window, introducing a gradient sensitive weight distribution strategy, and carrying out adaptive dynamic filtering on the salinity data; generating an initial hyper-parameter combination of the GBDT model; inputting salinity data subjected to adaptive dynamic filtering into the model, optimizing an initial hyper-parameter combination of the model by using a grey wolf optimization algorithm, obtaining an optimal hyper-parameter combination through a dynamic search strategy, and obtaining a final salinity correction value at the same time; and carrying out index verification on the final salinity correction value, and when the salinity does not reach the standard, correcting a model hyper-parameter or a self-adaptive dynamic filtering parameter until the salinity reaches the standard. According to the method disclosed by the invention, the physical abrupt change signal of the spring layer region is reserved to the greatest extent while the noise is suppressed, and the accuracy of salinity profile correction is remarkably improved.

The invention relates to the technical field of image processing, can be applied to business scenes of agricultural insurance, medical health and the like, and discloses a data processing method, device and equipment based on a spectrum association weight and a medium, and the method comprises the steps: obtaining original spectrum data, carrying out the preprocessing of the original spectrum data, and generating the preprocessed spectrum data; analyzing the relevance between spectral bands based on the preprocessed spectral data, and generating a spectral relevance weight; determining activation state distribution of a plurality of filters according to the spectrum correlation weight, selecting an activation filter based on the distribution to perform feature extraction on the data, and generating screening features; and inputting the screened features into an intelligent decision model to generate a target decision result. According to the method, through a dynamic filter activation strategy, the calculation complexity can be effectively reduced, and meanwhile high target detection precision is kept. Through deep analysis and optimization of the correlation between spectral bands, the robustness in small target detection and dynamic change environments is improved.

The invention relates to the technical field of the Internet of Things, and discloses an animal physiological feature data acquisition system and analysis method based on the Internet of Things, and the method comprises the following steps: cleaning and standardizing original sensor data, including filling missing data, eliminating noise interference through a dynamic filtering technology, unifying the dimension of multi-source data, and analyzing the data; the method comprises the following steps of: acquiring a Beidou time service time reference, eliminating redundant features, interpolating and aligning asynchronous sampling data to the Beidou time service time reference, correcting a time sequence phase difference of physiological and behavior data through a dynamic path matching technology, and correcting GPS / Beidou positioning drift in combination with Kalman filtering. Through multi-source sensing fusion and an edge-fog-cloud cooperative computing architecture, the effective collection rate of animal physiological data is achieved, abnormal response is delayed and compressed, the flexible energy supply technology and dynamic communication optimization are combined, the endurance and communication success rate is maintained in a pasture, and the false alarm rate is reduced compared with a traditional method; common diseases such as abnormal body temperature, digestive system diseases and the like are accurately warned and covered.

The invention relates to the technical field of sound equipment, and discloses a light follow-up sound equipment based on data analysis and a control system thereof, and the system comprises an audio signal processing module which is used for carrying out the noise filtering of audio data, and extracting the real-time audio signal intensity; the volume regulation and control module is connected with the audio signal processing module and used for regulating and controlling the volume according to the intensity of the real-time audio signal after noise filtering; the frequency analysis module is used for analyzing frequency characteristics of the audio data so as to determine corresponding light color parameters; according to the system, the audio is processed by adopting dynamic filtering and weighting algorithms, and the signal intensity is accurately extracted to regulate and control the volume. High synchronization of light and music is realized through delay compensation and adaptive threshold analysis frequency characteristics in combination with multi-stage dimming, and meanwhile, environment interference correction and system self-inspection are realized, so that the audio-visual experience of a user is improved.

The invention discloses a convolutional neural network information processing method for heart function dynamic monitoring, and belongs to the technical field of medical detection and monitoring. Comprising the following steps that multi-mode electrocardiosignal data and current physiological state parameters of a patient are obtained, and initial configuration of the intelligent heart function monitoring device is obtained; determining the signal quality grade of each signal channel, and generating a dynamic filtering adjustment strategy of the multi-modal signal acquisition module; configuring a feature extraction strategy of a double-branch convolutional neural network module based on the signal features of the target analysis signal segment and the physiological state parameters; performing multi-scale time sequence feature extraction and frequency domain autonomic nerve regulation feature extraction on the target analysis signal segment by using a double-branch convolutional neural network module according to a feature extraction strategy; executing arrhythmia classification, heart rate variability parameter quantification, heart function evaluation grade and graded early warning tasks, and outputting multi-stage intelligent early warning information from normal monitoring, potential risk and abnormal early warning to an emergency state.

The invention provides a data processing method, electronic equipment and a storage medium, and the method comprises the steps: receiving a query request which comprises a target data table participating in a connection operation; executing a logic plan according to the query request, wherein the execution of the logic plan comprises the following steps: constructing a data filtering condition corresponding to the column statistical information; wherein the column statistical information is information obtained by performing statistics on a column value range of columns contained in the target data table in advance; in the execution process of a physical plan corresponding to the logic plan, partition cutting and / or dynamic filtering are / is conducted on the target data table according to the data filtering condition, a processed target data table is generated, connection operation is conducted on the processed target data table, and an execution result is obtained. According to the method, the data processing efficiency can be improved, and the query performance is improved.

The invention discloses an SAR image ship small target robust detection method and system based on improved YOLOv11, a storage medium and electronic equipment, and the method comprises the following steps: embedding an S2-MLPv2 module at the tail end of a backbone network, and suppressing speckle noise through spatial displacement operation and dynamic filtering; the SAFMN module is used for replacing traditional up-sampling, and the multi-scale feature fusion capability is enhanced in combination with a deformable convolution and gating mechanism; a SimAM non-parameter attention module is introduced, and ship texture and geometric features are adaptively focused based on an energy function; designing a TSIoU loss function, fusing a central point diagonal distance measurement and an end point distance measurement, and optimizing bounding box regression precision; a slice auxiliary reasoning strategy is applied, and the small target recall rate is increased through multi-scale slicing and parallel detection. The method solves the problems of strong noise interference, insufficient small target feature extraction, poor multi-scale adaptability and low convergence efficiency in the prior art.

The invention discloses a scene understanding method and system for multi-level image feature extraction, and the method comprises the steps: obtaining input image data, and carrying out the data preprocessing, and obtaining the preprocessed image data; extracting fine-grained local scene features of the preprocessed image data based on DFFormer to obtain local scene features; extracting global features based on MAE enhancement to obtain global scene features; fusing the local scene features and the global scene features to obtain fused features; and performing classified output according to the fusion features. The limitation of an existing scene understanding method in the aspect of extracting fine-grained local features and global context information is solved. By combining the dynamic filtering capability of the DFFormer and the global feature representation capability of the ViT enhanced by the MAE, efficient extraction and fusion of image features are realized, and more accurate input features are provided for a scene understanding task.

The invention relates to the technical field of building information, and provides a knowledge base construction and retrieval method and system based on a multi-source text in the building field, and the method comprises the following steps: a knowledge base construction stage: constructing a multi-dimensional metadata feature vector for a multivariate text based on a standard classification index table; the method comprises the following steps of: converting and segmenting a document, splicing an end clause with all superior title texts by utilizing a context inheritance algorithm to form a text unit with complete semantics, and performing dynamic filtering based on an analyzed query intention and a metadata vector at a user retrieval stage; then, in the screening set, performing fusion calculation on semantic vector similarity, keyword matching degree and authority offset weight based on effectiveness attribute and implementation time, and performing mixed retrieval and reordering on the text units; and finally selecting a text unit according to a sorting result and inputting the text unit into the large language model to generate answers. According to the method, high-precision and high-compliance intelligent retrieval and question answering of building domain knowledge are realized.

The invention provides an NL2SQL database table dynamic screening method and system based on multi-strategy fusion. The method comprises the steps that a preliminary candidate set is generated, a semantic encoder is used for encoding a user natural language problem and table and column information in a database into vectors, the semantic similarity is calculated, a preset number of tables are screened out from a whole database table based on the similarity, and a preliminary candidate table set is formed; performing deep analysis and reordering, performing multi-dimensional analysis on the tables in the preliminary candidate table set, and calculating a graph relationship compactness score and a content correlation score of each table; and performing fusion decision, inputting the semantic similarity score, the graph relationship compactness score and the content correlation score of each table into a fusion model to obtain a final comprehensive score of each table, and screening out a final correlation table set based on the final comprehensive score. The method has high accuracy and high recall rate, the generation speed and response efficiency of the NL2SQL system are improved, meanwhile, the robustness is high, and the practicability and generalization ability of the system are improved.

The invention discloses a laser stripe center line extraction method based on a dynamic compensation coupling gray gravity center method, and relates to the technical field of structured light measurement. The method comprises the following steps: acquiring multiple groups of dynamic data source graphs of a measured object at different belt speeds of a conveyor belt; the dynamic data source image comprises multiple frames of laser stripe images; for each frame of laser stripe image, carrying out vibration displacement compensation on the height of the measured object in the laser stripe image, and after the vibration displacement compensation is carried out, removing laser stripe deformation in the laser stripe image through speed displacement compensation to obtain a recovered laser stripe image; the laser stripe deformation is generated by the movement of the measured object; and designing an improved gray gravity center method based on gradient constraint segmentation weighting mechanism and dynamic filtering fusion, and processing the recovered laser stripe image through the improved gray gravity center method to generate a laser stripe center line. The method can reduce the extraction error of the laser stripe center line under the dynamic working condition.

The invention, which relates to the technical field of radar anti-interference algorithm optimization, discloses a radar anti-interference algorithm optimization platform comprising an environment sensing module, an interference classification module, a dynamic filtering module, a waveform optimization module, an array processing module and an efficiency feedback module. The environment sensing module is used for carrying out multi-dimensional sampling on radar receiving signals by adopting a broadband digital channelization technology to obtain original electromagnetic environment data; the interference classification module is used for analyzing the original electromagnetic environment data by adopting a deep convolutional neural network to obtain a feature parameter set; the dynamic filtering module is used for carrying out adaptive processing on the characteristic parameter set by adopting an improved RLS-NLMS hybrid algorithm to obtain a baseband signal; the waveform optimization module is used for carrying out waveform parameter inversion on the baseband signal by adopting a genetic algorithm to obtain a waveform code; and the array processing module is used for performing spatial domain synthesis on the waveform codes by adopting a distributed MVDR algorithm to obtain an anti-interference beam forming weight.

The invention relates to the technical field of power transmission line sag measurement, in particular to a power transmission line sag measurement method based on laser point cloud data, which comprises the following steps: controlling a laser radar module and a multispectral imaging module to perform time-space synchronous data acquisition on a power transmission line area, and generating an auxiliary three-dimensional point cloud through a data fusion and compensation module; fusing the laser point cloud and the auxiliary three-dimensional point cloud by adopting an adaptive weighted average algorithm to generate a fused point cloud; performing dynamic filtering processing on continuous multi-frame point cloud data through a dynamic filtering module based on Kalman filtering and an established conductor dynamic model, filtering out high-frequency vibration components, and extracting a steady-state position point cloud of the conductor; the sag value of the power transmission line is calculated through the sag calculation module, so that cooperative sensing and data adaptive fusion of the multi-source sensor in a severe environment are completed, recursive filtering processing is performed on continuous multi-frame point cloud data, and a more real and more accurate data basis is provided for sag calculation.

The invention provides a high-fidelity anti-compression image watermarking method and system based on spectrum-airspace decoupling, and belongs to the field of information security. Firstly, the watermark information is mapped and remodeled; a watermark encoder based on multi-granularity spectrum-spatial domain feature decoupling is constructed, discrete cosine transform is introduced to filter out high-frequency components, Haar wavelet transform is adopted to realize lossless downsampling, a fast Fourier transform dynamic filter is combined to capture global semantic features, and local texture details are combined through multi-scale spatial domain volume accumulation; designing a physical perception and visual self-adaptive dual embedding strategy, and anchoring watermark energy to an anti-compression brightness channel; constructing an anti-attack layer containing differentiable JPEG compression simulation and mixed noise simulation, and participating in network training; and constructing a decoder and designing a loss function to carry out network optimization. According to the method, the robustness of the watermark under strong compression and complex black box attacks is improved, and extremely high visual imperceptibility is realized through physical and visual constraints.

The invention relates to the technical field of audio signal processing, and provides a self-adaptive frequency compensation system for small-sized multichannel sound equipment and a control method thereof, and the self-adaptive frequency compensation system for the small-sized multichannel sound equipment comprises an audio signal acquisition module, a frequency analysis module and a dynamic filtering compensation module which are electrically connected with one another in a communication manner; the audio signal acquisition module is used for acquiring an audio signal at a preset sampling rate through the ADC module; the frequency analysis module is used for executing FFT (Fast Fourier Transform) on the acquired audio signals to calculate energy distribution of corresponding frequency bands and identify missing or attenuated frequency ranges; and the dynamic filtering compensation module is used for selecting an FIR (Finite Impulse Response) or IIR (Infinite Impulse Response) filter according to a frequency analysis result, dynamically adjusting a filter coefficient and a gain value, performing real-time filtering processing on the audio signal and outputting a compensated audio signal. The missing frequency is identified and compensated by performing analysis, filtering, equalization and other processing on the audio signal.

The application discloses a structure point cloud data multi-scale filtering method considering environmental dynamic influence, which firstly adopts a 'K' nearest field method considering structure dynamic influence to perform dynamic filtering processing on original point cloud data, removes scattered outliers in the original point cloud data, and then divides the point cloud data into flat regions and mutation regions based on an improved principal component analysis algorithm (LMSR-PCA), adopts statistical filtering based on local surface fitting for the flat regions, and adopts spatial adaptive bilateral filtering for the mutation regions. The application is used to solve the problems that the existing point cloud data processing method cannot efficiently process massive point cloud data with complex curve characteristics, and cannot solve the problem of excessive smoothing caused by the loss of edge features in the noise reduction process of point cloud data with complex curve characteristics.

The invention discloses an intelligent tent data acquisition method and a rescue early warning platform, and belongs to the technical field of data acquisition. Environmental monitoring, attitude and image multi-source data are acquired through distributed nodes, and through dynamic filtering and time domain alignment preprocessing, data are fused by using a dynamic weight distribution matrix; abnormality is detected in combination with a dynamic threshold model based on historical data and a real-time environment, and transmission is performed through a multi-channel protocol after continuous period early warning is triggered. The rescue early warning platform comprises a data receiving module, a data processing server and a rescue information distribution system with differential broadcasting and receipt retransmission functions. Through multi-source sensor fusion and a dynamic weight distribution matrix, the comprehensiveness and precision of environmental risk monitoring are remarkably improved; and a dynamic threshold model and a redundant transmission protocol are combined, so that the false alarm rate is reduced, and second-level early warning response is realized.

The invention relates to the technical field of digital integrated circuit verification, in particular to a large-scale chip post-simulation log file intelligent analysis method and device and a medium. The method comprises the following steps: performing data cleaning on a post-simulation log file; dividing the cleaned log file into a plurality of sub-file blocks according to timestamps, starting multiple threads to process each sub-file block in parallel, counting the types and quantity of time sequence violations, extracting related logs and generating a time sequence violation file; classifying and analyzing the time sequence violation file by utilizing a machine learning algorithm; dynamically filtering the time sequence violation file according to a preset rule, screening key violation information, and repairing time sequence violation caused by signal delay; and in combination with a chip circuit structure and a signal transmission path, positioning a root module or node of a time sequence violation, formulating an optimization scheme and verifying a repair effect. According to the method, efficient and accurate analysis of the post-simulation log file of the large-scale chip is realized, and the efficiency and accuracy of chip verification are improved.

The invention discloses a visual Token compression method and device based on double-path fusion, and the method comprises the steps: obtaining an initial visual Token through employing a visual encoder; obtaining a learnable query vector, and adopting an attention mechanism to enable the learnable query vector to interact with the initial visual Token to obtain a plurality of context Token; analyzing a self-attention map of the initial visual Token in the visual encoder, so as to screen out a plurality of detail Token according to the self-attention map; obtaining a text prompt Token, and calculating a task correlation score of each detail Token by taking the text prompt Token and the plurality of context Token as a query; dynamically filtering or weighting the plurality of details Token according to the task correlation score to obtain a plurality of filtered details Token; the plurality of filtered detail Tokens are spliced with the plurality of context Tokens, so that a compressed visual Token can be obtained; therefore, the key global and local visual information can be intelligently reserved while the high compression ratio is realized and the calculation cost is reduced.

The invention discloses a centrifugal machine dynamic filtering efficiency improving method based on artificial intelligence optimization control, and relates to the technical field of centrifugal machine filtering efficiency optimization. Constructing an edge-cloud collaborative architecture system; the sensing layer deploys various sensors to acquire equipment operation data; the edge calculation layer processes the data and makes a decision; the network transmission layer ensures reliable transmission of data; the cloud platform is used for model training and global optimization; through preprocessing and feature extraction of collected data, the state is evaluated by using a hybrid model fusing deep learning and physical simulation, control parameters are optimized based on a multi-objective optimization function in combination with fuzzy logic and an adaptive algorithm, and dynamic feedback adjustment is realized by using electro-hydraulic servo and machine vision. According to the technology, intelligent operation of the centrifugal machine is achieved, compared with traditional control, the filtering efficiency is improved, energy consumption is reduced, the service life of filter cloth is prolonged, equipment fault diagnosis is more accurate, maintenance response is faster, production benefits are improved, and product market competitiveness is enhanced.

The invention discloses a dynamic filtering and beamforming method for improving the anti-interference capability of a secondary radar. The method comprises the following steps: initializing original data; periodically monitoring the interference congestion degree of the downlink response signal of the secondary radar system in the current radio environment, and judging the interference intensity; dynamically adjusting the transmitting power and the PRF of an uplink interrogation signal of the secondary radar system; notifying the air target responder to reduce the sending rate and the transmitting power of a downlink response signal through the downlink control instruction; the general orientation of the interference source is determined in combination with external monitoring information, adaptive beamforming is performed based on the array antenna, and null is formed in the general direction of the interference source to realize interference suppression of a spatial domain; and performing dynamic filtering processing on the receiving signal after beam forming processing, extracting an effective target response signal sequence, and reconstructing the effective target response signal sequence into complete target response information data according to a predetermined protocol format. According to the invention, the accuracy and reliability of target signal detection and the anti-interference performance of the system are improved.

The invention relates to the technical field of safety monitoring of power transmission equipment, in particular to an intelligent early warning nut and a monitoring method thereof.The nut comprises a quantitative anti-interference sensing and dynamic filtering module, a verification parameter is dynamically calculated based on an interference-verification quantitative model by synchronously collecting loosening distance, vibration and electromagnetic interference data, and the verification parameter is obtained; a real loosening event is accurately identified; the environment coupling trend quantitative prediction module is used for fusing multi-environment factor dynamic weighting such as humidity, wind speed and temperature difference after real loosening is judged, and predicting a future loosening distance through an environment-loosening quantitative correlation model; and the scenarized risk grading response module is used for calculating a scenarized risk quantized value R and triggering grading early warning in combination with the prediction result and the structural importance weight of the bolt mounting position, and generating a strategy instruction containing a maintenance priority. According to the invention, the problems of high false alarm rate, incapability of predictive maintenance and short endurance in the prior art are solved, and accurate sensing, intelligent prediction and graded early warning of the bolt loosening state are realized.